Reverse time migration (RTM) is an indispensable but computationally intensive seismic exploration technique. Graphics processing units (GPUs) by NVIDIA■offer the option for parallel computations and speed improvemen...Reverse time migration (RTM) is an indispensable but computationally intensive seismic exploration technique. Graphics processing units (GPUs) by NVIDIA■offer the option for parallel computations and speed improvements in such high-density processes. With increasing seismic imaging space, the problems associated with multi-GPU techniques need to be addressed. We propose an efficient scheme for multi-GPU programming based on the features of the compute-unified device Architecture (CUDA) using GPU hardware, including concurrent kernel execution, CUDA streams, and peer-to-peer (P2P) communication between the different GPUs. In addition, by adjusting the computing time for imaging during RTM, the data communication times between GPUs become negligible. This means that the overall computation effi ciency improves linearly, as the number of GPUs increases. We introduce the multi-GPU scheme by using the acoustic wave propagation and then describe the implementation of RTM in tilted transversely isotropic (TTI) media. Next, we compare the multi-GPU and the unifi ed memory schemes. The results suggest that the proposed multi- GPU scheme is superior and, with increasing number of GPUs, the computational effi ciency improves linearly.展开更多
基金supported by the National Key R&D Program of China(2017YFC0602204-01)NSFC(Grant Nos.41530321 and 41104083)
文摘Reverse time migration (RTM) is an indispensable but computationally intensive seismic exploration technique. Graphics processing units (GPUs) by NVIDIA■offer the option for parallel computations and speed improvements in such high-density processes. With increasing seismic imaging space, the problems associated with multi-GPU techniques need to be addressed. We propose an efficient scheme for multi-GPU programming based on the features of the compute-unified device Architecture (CUDA) using GPU hardware, including concurrent kernel execution, CUDA streams, and peer-to-peer (P2P) communication between the different GPUs. In addition, by adjusting the computing time for imaging during RTM, the data communication times between GPUs become negligible. This means that the overall computation effi ciency improves linearly, as the number of GPUs increases. We introduce the multi-GPU scheme by using the acoustic wave propagation and then describe the implementation of RTM in tilted transversely isotropic (TTI) media. Next, we compare the multi-GPU and the unifi ed memory schemes. The results suggest that the proposed multi- GPU scheme is superior and, with increasing number of GPUs, the computational effi ciency improves linearly.
